Senior Solutions Architect – Simulations, Clinical Sciences, Autonomous Lab
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Job Description
Senior Solutions Architect driving innovation with GPU-accelerated simulations for clinical sciences and autonomous labs. Collaborating with pharmaceutical companies and software builders for AI implementation and optimization.
Responsibilities:
- Guide customers through the end-to-end adoption of GPU-accelerated AI, from requirements gathering and proof-of-concept development to deployment, integration, and ongoing optimization.
- Architect libraries such as GPU-accelerated solvers for quantitative systems pharmacology and CPU-to-GPU migration of scientific workloads.
- Perform low-level CUDA optimization, including custom kernels to accelerate simulation and inference workloads in drug discovery.
- Building physical AI and robotics solutions for autonomous labs and biomanufacturing such as sim-to-real VLA pipelines, real-time control layers, and integration of perception, control, and policy stacks on NVIDIA platforms.
- Designing and deploying biomedical agentic AI systems, such as graph-based retrieval, multi-hop clinical reasoning, and persistent agent memory.
- Keeping up to date on AI advancements in healthcare, including domain-specific models, robotics, and agentic frameworks.
- Engaging with life science executives, IT leaders, data scientists, and developers to drive adoption of NVIDIA AI stack.
- Sharing findings through training sessions, white papers, blog posts, and conference talks.
Requirements:
- MS, PhD, or equivalent experience in Computer Science, Biomedical Engineering, Computational Biology, Computational Chemistry, Robotics, or related fields with strong applied experience.
- 8+ years of experience.
- Proven track record in software development for AI/ML, scientific computing, GPU acceleration, or robotics applied to healthcare or life sciences.
- Hands-on experience across at least two of the three focus areas: GPU-accelerated scientific simulation, sim-to-real robotics, and end-to-end agentic AI.
- Proficiency in Python and AI/ML frameworks (PyTorch, LangChain, or custom).
- Experience with C/C++ and CUDA strongly preferred.
- Experience deploying and scaling GPU-accelerated solutions in cloud or HPC environments (OCI, AWS, Azure, or on-prem clusters).
- Excellent communication skills with the ability to present complex technical concepts to both technical and non-technical audiences.
- Up to 20% travel may be required for on-site customer engagements.
Benefits:
- Eligible for equity and benefits


















